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| import tensorflow as tf
import os
import pickle
import numpy as np
CIFAR_DIR = "cifar-10-batches-py"
def load_data(filename):
with open(filename, 'rb') as f:
data = pickle.load(f, encoding='iso-8859-1')
return data['data'], data['labels']
class CifarData:
def __init__(self, filenames, need_shuffle):
all_data = []
all_labels = []
for filename in filenames:
data, labels = load_data(filename)
for item, label in zip(data, labels):
all_data.append(item)
all_labels.append(label)
self._data = np.vstack(all_data)
self._data = self._data / 127.5 - 1
self._labels = np.hstack(all_labels)
self._num_examples = self._data.shape[0]
self._need_shuffle = need_shuffle
self._indicator = 0
if self._need_shuffle:
self._shuffle_data()
def _shuffle_data(self):
p = np.random.permutation(self._num_examples)
self._data = self._data[p]
self._labels = self._labels[p]
def next_batch(self, batch_size):
end_indicator = self._indicator + batch_size
if end_indicator > self._num_examples:
if self._need_shuffle:
self._shuffle_data()
self._indicator = 0
end_indicator = batch_size
else:
raise Exception("have no more examples")
if end_indicator > self._num_examples:
raise Exception("batch size is larger than all examples")
batch_data = self._data[self._indicator: end_indicator]
batch_labels = self._labels[self._indicator: end_indicator]
self._indicator = end_indicator
return batch_data, batch_labels
x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])
w = tf.get_variable('w', [x.get_shape()[-1], 10],
initializer=tf.random_normal_initializer(0, 1))
b = tf.get_variable('b', [10], initializer=tf.constant_initializer(0.0))
y_ = tf.matmul(x, w) + b
'''
p_y_1 = tf.nn.softmax(y_)
y_one_hot = tf.one_hot(y, 10, dtype=tf.float32)
loss = tf.reduce_mean(tf.square(y_one_hot - p_y_1))
'''
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
predict = tf.math.argmax(y_, 1)
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
with tf.name_scope('train_op'):
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
train_filenames = []
for i in range(1, 6):
train_filenames.append(os.path.join(CIFAR_DIR, 'data_batch_%d' % i))
test_filenames = [os.path.join(CIFAR_DIR, 'test_batch')]
train_data = CifarData(train_filenames, True)
test_data = CifarData(test_filenames, False)
init = tf.global_variables_initializer()
batch_size = 10
train_steps = 10000
test_steps = 200
with tf.Session() as sess:
sess.run(init)
for i in range(train_steps):
batch_data, batch_labels = train_data.next_batch(batch_size)
loss_val, acc_val, _ = sess.run(
[loss, accuracy, train_op],
feed_dict={
x: batch_data,
y: batch_labels
}
)
if (i + 1) % 500 == 0:
print('[Train] Step : %d, loss %4.5f, acc: %4.5f' % (i, loss_val, acc_val))
all_test_acc_val = []
if (i + 1) % 5000 == 0:
test_batch_data, test_batch_labels = test_data.next_batch(batch_size)
test_acc_val = sess.run(
[accuracy],
feed_dict={
x: test_batch_data,
y: test_batch_labels
}
)
all_test_acc_val.append(test_acc_val)
test_acc = np.mean(all_test_acc_val)
print('[Test] Step: %d, acc: %4.5f' % (i + 1, test_acc))
|
